Today, handwriting is becoming an increasingly popular method for inputting data to data handling units, especially to mobile phones and Personal Digital Assistants (PDAs). In order to handle the inputted data, the handwriting must be recognized and interpreted. Most existing methods for recognizing handwriting require that the characters that are to be inputted are written one by one and are separately recognized. An example of such a method is provided in U.S. Pat. No. 4,731,857, but the most famous is Graffiti®, manufactured by Palm, Inc.
In order to speed up input of data it is desired that cursive handwriting is allowed. However, recognition of cursive handwriting is far more complex than recognition of separate characters. The increase in complexity for cursive handwriting recognition is owed to the problem of segmenting connected characters, i.e. to identify the transition from one character to another within the handwritten pattern. Errors in cursive handwriting recognition may hence come in two levels, that is errors in segmentation and errors in recognition of the separated characters, which greatly complicate the construction of a lucid sequential recognition system.
Methods for recognition of cursive handwriting generally suffer from the problem that there are a lot of possible segmentations between adjacent characters forming a combinatorial explosion of possible segmentations of a handwritten pattern.
Most commercial systems today therefore employ complicated statistical systems using neural networks and hidden markov models with integrated dictionaries. Examples of such systems are presented in P. Neskovic and L. Cooper, “Neural network-based context driven recognition of on-line cursive script”, Seventh International Workshop on Frontiers in Handwriting Recognition Proceedings, p. 352-362, September 2000 and M. Schenkel and I. Guyon, “On-line cursive script recognition using time delay networks and hidden markov models”, Machine Vision and Applications, vol. 8, pages 215-223, 1995. A major setback of these systems is that they are large and require large training sets. Furthermore they are highly dependent on the dictionary used.